Poster — Wed Eve—39: Using DOSRZnrc to Study the SFD Si‐Diode for Small Field Relative Dosimetry
Bibliographic record
Abstract
Purpose: The goal of this work was to study the SFD Si‐diode (IBA Dosimetry, previously Scanditronix/Wellhofer) for small field dosimetry. Methods and Materials: The EGSnrc user code DOSRZnrc was used to construct a geometrically accurate model of the detector. The active volume was modelled as pure silicon ( ) and 6HSiC ( ). For field sizes between 0.5 and 5.0 cm, water tank simulations were run and validated experimentally for isocentric detector placement at depths of 1.5, 5.0 and 10.0 cm. Detector response was investigated and correction factors calculated according to Capote et al (Med. Phys. 31 2416–22) and Alfonso et al (Med. Phys. 35 5179–86). Results: The SFDSi simulated ROFs were generally higher than the experimental values yet still within the statistical uncertainty of 1.3 to 1.5%. However, for the two smallest field sizes the data was high by as much as 3.0%. The simulated ROFs revealed no such disagreement with experiment and were statistically equivalent to the measured data. Conclusion: The active volume of the SFD detector is most likely a substrate very similar to 6HSiC and should not be modelled as pure silicon, modelling the detector as a “chip in water” may be considered a reasonable approximation but does hinge on the correct choice of material for the active volume and it appears no correction factor is required for small field relative output factors measured with the SFD Si‐diode detector, however a reduction in the statistical uncertainty would be desirable.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 0.006 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".